Research Trends in Mobile Learning in Higher Education: A Systematic Review of Articles (2011 – 2015)

Abstract

p class="3">The potential and use of mobile devices in higher education has been a key issue for educational research and practice since the widespread adoption of these devices. Due to the evolving nature and affordances of mobile technologies, it is an area that requires ongoing investigation. This study aims to identify emerging trends in mobile learning research in higher education in order to provide insights for researchers and educators around research topics and issues for further exploration. This study analysed the research themes, methods, settings, and technologies in mobile learning research in higher education from 2011 to 2015. A total of 233 refereed articles were selected and analysed from peer reviewed journals. The results were compared to three previous literature review-based research studies focused between 2001 and 2010 to identify similarities and differences. Key findings indicated that: (a) mobile learning in higher education is a growing field as evidenced by the increasing variety of research topics, methods, and researchers; (b) the most common research topic continues to be about enabling m-learning applications and systems; and (c) mobile phones continue to be the most widely used devices in mobile learning studies, however, more and more studies work across different devices, rather than focusing on specific devices.</p

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International Review of Research in Open and Distributed Learning
Volume 18, Number 7
November – 2017
Research Trends in Mobile Learning in Higher
Education: A Systematic Review of Articles (2011 –
2015)
Greig Krull and Josep M Duart
Universitat Oberta de Catalunya
Abstract
The potential and use of mobile devices in higher education has been a key issue for educational research
and practice since the widespread adoption of these devices. Due to the evolving nature and affordances
of mobile technologies, it is an area that requires ongoing investigation. This study aims to identify
emerging trends in mobile learning research in higher education in order to provide insights for
researchers and educators around research topics and issues for further exploration. This study analysed
the research themes, methods, settings, and technologies in mobile learning research in higher
education from 2011 to 2015. A total of 233 refereed articles were selected and analysed from peer
reviewed journals. The results were compared to three previous literature review-based research studies
focused between 2001 and 2010 to identify similarities and differences. Key findings indicated that: (a)
mobile learning in higher education is a growing field as evidenced by the increasing variety of research
topics, methods, and researchers; (b) the most common research topic continues to be about enabling
m-learning applications and systems; and (c) mobile phones continue to be the most widely used devices
in mobile learning studies, however, more and more studies work across different devices, rather than
focusing on specific devices.
Keywords: mobile learning, research trends, research methods, pedagogical issues, higher education
Introduction
Many higher education institutions are implementing mobile learning to provide flexibility in learning.
It is expected that this will continue to be a growing trend with the proliferation of wireless devices and
technologies. It is expected that the next generation of mobile learning will be ubiquitous and learners
themselves will be more mobile and able to learn using multiple devices (Ally & Prieto-Blázquez, 2014).
Although there are a number of interpretations of what is meant by mobile learning, this study makes

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use of the definition by O’Malley et al. (2005) as “any sort of learning that happens when the learner is
not at a fixed, predetermined location, or learning that happens when the learner takes advantage of the
learning opportunities offered by mobile technologies.” (p. 7).
Mobile devices tend to drive new research opportunities in mobile learning because of the rate of
changes in technologies. In addition to devices, communication technologies have also changed, shifting
the focus of research (Parsons, 2014). For example, social media and messaging “apps” are
commonplace. The development and usage patterns of mobile technologies in education change quickly.
This means that regular analysis is required of “trends in mobile device types and functionality, along
with learner types and the use of mobile devices in various disciplines and courses” (Wu et al., 2012, p.
818). The research purposes and methods used in studies are important because they influence how
research results are shared, interpreted and used (Wingkvist & Ericsson, 2011). Review studies can help
to identify progress in the field and offer guidelines for the design of future research (Frohberg, Göth, &
Schwabe, 2009). Understanding the trends in research studies can also help higher education policy
makers in making decisions regarding technology and teaching and learning (Wu et al., 2012).
This paper provides a systematic review of mobile learning research in higher education from 2011 to
2015. It begins with an analysis of previous review studies in order to provide the basis of comparison
with similar studies. The research purpose and questions are then described. The next section discusses
the methodology used to conduct the review study. This is followed by the presentation of the results of
the study, with a comparison to three previous studies. The final section provides a discussion of the
findings of the review study.
Previous Studies
A number of review studies have been conducted in recent years in an attempt to explore and provide
insights into the growing body of knowledge in mobile learning. One of the first reviews in mobile
learning provided an activity-focused perspective of case studies in the use of mobile technologies for
education (Naismith, Lonsdale, Vavoula, & Sharples, 2004). Cheung and Hew (2009) conducted a
review of research methodologies used in mobile learning in school and higher education settings. They
reviewed 44 articles published until the end of 2008 and found that descriptive research was the most
dominant research method and questionnaires were the most used data collection method. Frohberg,
Göth, and Schwabe (2009) conducted a review of 109 mobile learning projects to evaluate and categorise
them against a mobile learning task model. Hwang and Tsai (2011) conducted a study of research trends
in mobile and ubiquitous learning by reviewing 154 articles from six major technology-enhanced
learning journals from 2001 to 2010. They found that the number of studies increased significantly over
the period. They also found that higher education students were the most frequent learning populations
and that most studies did not focus on a specific learning domain. Hung and Zhang (2012) examined
mobile learning trends between 2003 and 2008 by using text-mining techniques to conduct a meta-
trend analysis of 119 articles. They similarly found that studies in mobile learning increased rapidly over
that period. They also found that many studies focused on the effectiveness of mobile learning but there
was increasing focus on evaluation and systems development. Wu et al. (2012) recognised the value of
these two previous studies, but felt further examination was required “from the standpoint of research
purposes, methodologies, and outcomes” (p. 817). The authors used a meta-analysis approach to

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systematically review 164 mobile learning studies published between 2003 and 2010. They also found
most research purposes focussed on effectiveness and system design, but also found that surveys and
experimental methods were the most used research methods and that the research outcomes in studies
were significantly positive.
Systematic reviews have also been conducted on conference proceedings. Wingkvist and Ericsson (2011)
surveyed 114 papers presented at the World Conference on Mobile Learning (mLearn) conferences in
2005, 2007, and 2008. The focus of the review was on research purposes and research methods. They
found that research methods were evenly distributed, with the exception of basic research (development
of new theories). In terms of research purpose, the majority of papers were descriptive research,
followed by developmental and understanding research. The lack of evaluative research papers was
found to be a problem (Wingkvist & Ericsson, 2011).
A number of review studies have also been conducted to investigate a particular aspect or theme related
to mobile learning. Wong and Looi (2011) conducted a review of mobile-assisted seamless learning
related literature between 2006 and 2011. Baran (2014) studied the literature to “fill the gap” on mobile
learning research in teacher education programmes. Song (2014) investigated methodological issues in
Mobile Computer-Supported Collaborative Learning (mCSCL) research between 2000 and 2014. Liu et
al. (2014) reviewed 63 articles in K-12 education between 2007 and 2012. Hsu and Ching (2015)
reviewed 17 articles to categorise the models and frameworks developed specifically for mobile learning.
Alrasheedi and Capretz (2015) reviewed 19 articles to determine critical success factors affecting mobile
learning.
Parsons (2014) noted the number of previous reviews, yet highlighted that most reviews tended to focus
on a specific subset of the literature or a particular aspect of mobile learning. The purpose of his study
was to “provide a full-landscape view of the field of mobile learning” up to and including 2013 (p. 2).
Findings were presented in two forms. A timeline was used to highlight the evolution of mobile learning
through a series of significant “firsts.” Secondly, a mind map was used to summarise the key concerns
in the areas of research, technology, content, learning, and learner (Parsons, 2014).
Research Problem
The number of literature review-based studies and the results of these studies indicate a research field
that is growing and changing. Due to developments in technology, it is worth considering how the field
of mobile learning research is changing and how these studies are applied in higher education
specifically. Although several review studies (Hwang & Tsai, 2011; Wu et al., 2012) have found that the
majority of mobile learning studies take place within higher education, very few mobile learning review
studies have focussed solely on this sector. This study aims to analyse the research topics, methods,
settings, and technologies used in mobile learning research in higher education, published from January
2011 to December 2015. The research questions are:
1. What research methods have been used in mobile learning articles published from 2011 to 2015?
2. What are the research trends in terms of purposes, themes, and technologies?

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3. How do the findings relate to previous mobile learning reviews from 2001 to 2010?
Methodology
A systematic review provides a summary of the research literature, either quantitative or qualitative,
that uses explicit, replicable methods to identify and select relevant studies; and uses objective and
replicable techniques to analyse and summarise those studies (Cooper, 2010, as cited in Bernard,
Borokhovski, & Tamim, 2014). In order to ensure a systematic review process, this study followed the
seven steps suggested by Cooper (2010, as cited in Bernard et al., 2014) for conducting a systematic
review or meta-analysis:
1. Formulate the research problem.
2. Search the literature.
3. Gather information from studies.
4. Evaluate the quality of studies.
5. Analyse and integrate the outcomes of research.
6. Interpret the evidence.
7. Present the results.
These stages are neither mutually exclusive nor entirely distinct; rather, they should be viewed as key
steps in a continuous and iterative process (Cooper, 2010, as cited in Bernard et al., 2014). The first step
in conducting a systematic review is to formulate the research problem, which has been specified in the
section above.
Literature Search
The second step in a systematic review is to search the literature. A limitation may exist in this study,
referred to as publication bias (Bernard et al., 2014), as this study has not surveyed the “grey literature”
such as conference proceedings, technical reports, dissertations, and book chapters. However, the
search was limited to peer reviewed journal articles in order for better comparison between sources and
aligns with the search strategies by Hwang and Tsai (2011), Wu et al. (2012), Baran (2014), and Bozkurt
et al. (2015). Based on these studies, two databases were selected to ensure comprehensive data
collection: Scopus and ISI Web of Science. The starting point involved searching for a combination and
variation of the keywords “mobile learning” or “m-learning” and reviewing the results against the
following inclusion criteria:
 Must involve mobile learning as a primary condition,
 Must focus specifically on learning at the higher education level,
 Must be published in a peer reviewed journal between January 2011 and December 2015,
 Must be written in English, and

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 The full-text of the article must be publically available or available through the researchers’
institutional library subscriptions.
The first database searched was Scopus. A search of the keywords “mobile learning,” “m-learning,” or
“mlearning” in articles published between 2011 and 2015 resulted in 1024 results. The results were
filtered to remove non-journal sources (955 results remained) and non-English texts (937 results
remained). The researchers then discarded 373 results because they did not have access to the full text.
The remaining 564 results were assessed against the criteria that the primary focus of the article was
mobile learning and within higher education. A total of 348 articles did not meet these criteria, leaving
216 articles to be included in this study.
The second database searched was the ISI Web of Science (SCI/SSCI). A search of the keywords “mobile
learning,” “m-learning,” or “mlearning” in articles published between 2011 and 2015 resulted in 1703
results. The results were filtered to remove non-journal sources (698 results remained) and non-English
texts (578 results remained). The researchers then discarded 254 results because they did not have
access to the full text. The remaining 324 results were assessed against the criteria that the primary focus
of the article was mobile learning and within higher education. One hundred and sixty-nine articles did
not meet these criteria, leaving 155 articles to be included in this study. These were compared to the
results of the previous database search, and 138 duplicates were excluded and 17 results were added,
resulting in a total of 233 articles to be studied.
Information Gathering
The third and fourth steps in conducting a review are to gather the information from studies and
evaluate the quality of studies. The 233 articles were collected and organised with the bibliographic data
including article title, authors, journal, abstract, keywords, and publication year. Eleven additional
categories related to the articles were coded, based on the studies of Hwang and Tsai (2011), Wu et al.
(2012), Baran (2014), and Bozkurt et al. (2015). The categories were: (a) research purpose, (b) research
theme, (c) conceptual and theoretical background, (d) research method, (e) research design, (f) data
collection method, (g) target population, (h) learning domain/discipline, (i) learning setting, (j) type of
device, and (k) country. Two independent researchers then independently confirmed the coding for the
first six categories. Disagreements between the two coders were resolved through discussion and further
review of the disputed studies by the principal researchers. This review study targeted peer-reviewed
journal articles, which helps to ensure the relative rigour and quality of studies under review (Hsu &
Ching, 2015). The spreadsheet matrix with the 233 categorised articles can be accessed online.
Research Analysis
The fifth step is to analyse and integrate the outcomes of research. This study made use of content
analysis to analyse the data. Content analysis is a method of analysing documents and enables the
researcher to test theoretical issues to enhance understanding of the data (Elo & Kyngäs, 2008). Content
analysis can use a mix of quantitative and qualitative methods so that a combination of bibliometric and
categorical data can be used to reveal trends (Hung & Zhang, 2012). In order to answer our third
research question, the results were then compared to the results of this study with three previous
literature review studies (Hung & Zhang, 2012; Hwang & Tsai, 2011; Wu et al., 2012). It must be noted
that a direct comparison cannot be performed with each aspect of the studies due to differences in the

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approaches, timing, and methods used in this study, but that a useful comparison may still be drawn
between these studies.
Steps 6 and 7 of the systematic content review process are to interpret the evidence and present the
results. The next section of the paper presents the outcomes of this process. Two hundred and thirty-
three articles on mobile learning in higher education published from 2011 to 2015 were included in this
sample: for 2011 – 22 articles; for 2012 – 38 articles; for 2013 – 45 articles; for 2014 – 68 articles; and
for 2015 – 60 articles. The frequency of papers is apparent in the sample increase for each year under
study, except for the last.
Journals
These articles were published in 88 different journals. Table 1 shows the frequency of articles from
journals that have three or more articles in this study. Those journals that are open access are denoted
with an OA in brackets after the journal name.
Table 1
Distribution of Journals With Three or More Articles in This Study
Rank
Journals
Frequency
1
Computers & Education
19
2
The International Review of Research in Open and Distributed
Learning (OA)
18
3
Educational Technology & Society (OA)
13
3
International Journal of Interactive Mobile Technologies (OA)
13
5
Computers in Human Behavior
12
5
Turkish Online Journal of Educational Technology (OA)
12
7
British Journal of Educational Technology
11
7
Journal of Universal Computer Science (OA)
11
9
The Turkish Online Journal of Distance Education (OA)
7
10
Australasian Journal of Educational Technology (OA)
5
10
Electronic Journal of e-Learning (OA)
5
10
IEEE Transactions on Learning Technologies
5
13
International Journal of Mobile and Blended Learning
4
13
Research in Learning Technology (OA)
4
15
Journal of Asynchronous Learning Networks
3
15
Nurse Education Today
3
15
Language Learning and Technology (OA)
3
15
The International Journal of Educational Technology in Higher
Education (OA)
3
Countries
This study represented a wide range of developed and developing countries, for a total of 45 countries.
Country categorisation was based on the country where the research was conducted, rather than the
researcher’s affiliation. The countries with the most number of studies represented were United States
(26), United Kingdom (25), Taiwan (21), Spain (16), and Turkey (16). In terms of comparison with
studies from 2001-2010, these findings closely align to the findings of Hwang and Tsai (2011). In their
study, they found that the three countries that contributed the most number of studies were the United
States, Taiwan, and the United Kingdom, which is the same in this study. Hung and Zhang (2012) also
found the top two contributors to be Taiwan and the United States, although South Korea was third in

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their study. As an indication of the expansion of the field of mobile learning, the articles in the study by
Hwang and Tsai (2011) represented studies conducted in 25 countries, while in this study, 45 countries
were represented.
Results
Research Purposes
Each article was categorised according to its research purpose, adapted from the classification presented
by Wu et al. (2012). The original four purposes were: (1) Evaluate Effectiveness, (2) Design a Mobile
System, (3) Investigate the Affective Domain, or (4) Evaluate the Influence of Learner Characteristics.
A similar classification was provided by Hsu and Ching (2015). Two additional categories were added by
the researchers for this study: (5) Develop Theory and (6) Explore Potential, in order to better represent
all possible purposes. These categories were then defined as:
 Evaluate the effects: investigates whether mobile devices can improve or enhance student
learning.
 Explore the potential: explores how to use a new tool or how a new technology could be used
for learning (usually a small pilot or exploratory study).
 Investigate the affective domain: investigates the affective domain includes factors such as
student motivation, beliefs, attitudes, perceptions, and values.
 Design a system: designs frameworks or systems where the emphasis is on the development and
presentation of solutions.
 Develop theory: create or promote new pedagogical approaches, models, theories, or
frameworks of mobile learning.
 Influence of learner characteristics in the learning process: examines the influence of learner
characteristics such as age, gender, ability, experience, learning style, and culture.
As shown in Figure 1, the most common research purpose was found to be to evaluating effectiveness
(24%), followed by designing a mobile system (23%), and investigating the affective domain (19%). In
terms of comparison with 2001-2010, these findings are similar to those of Wu et al. (2012) in that
evaluating effectiveness was the most common method, followed by designing a mobile system.
However, studies investigating the affective domain, previously a very small research purpose in terms
of the number of studies, have become a greater point of focus.

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Figure 1. Distribution of studies by research purpose.
Themes
It is difficult to find a common list of themes within mobile learning as the categorisation of mobile
learning research depends on the focus of the interests of the researchers (Parsons, 2014). For example,
researchers such as Parsons (2014) and Hsu, Ching, & Snelson (2014) have provided different
categorisations. In this study, the researchers decided to adapt the themes proposed by the annual
International Conference on Mobile Learning Conference themes (http://mlearning-conf.org/). Figure
2 shows the distribution of research themes in studies from 2011-2015. Although several articles
contained overlapping themes, each article was categorised into one major theme for the purpose of this
review. Studies covered a wide range of themes within mobile learning in higher education. The most
common research theme focused on enabling m-Learning applications and systems (23%), followed by
socio-cultural context and implications of m-Learning (13%), and tools and technologies for m-Learning
(12%). No comparison can be done with the research studies from 2001-2010 as the research themes as
categorised in this study were not within the scope of the studies of Hwang and Tsai (2011), Hung and
Zhang (2012), and Wu et al. (2012).

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Figure 2. Distribution of studies by research theme.
Researchers continue to investigate a wide variety of research themes or topics. The most common
research theme for mobile learning in higher education is the wide variety of applications and systems
that are used to enable learning. Existing systems such as text messaging can be used to communicate
with or support students (Lim, Fadzil, & Mansor, 2011) or custom applications can be designed for
specific subjects (Wu, 2015). The next most common theme is the exploration and use of new tools and
technologies for mobile learning. These include specific devices such as smartphones (Gikas & Grant,
2013), tablets (Churchill & Wang, 2014; Engin & Donanci, 2015) and other devices. Researchers are also
interested in the social and cultural contexts that surround mobile learning (Arpaci, 2015; Viberg &
Gronlund, 2013). Educators are exploring how to use social media such as Twitter (Hsu & Ching, 2012)
for learning. Researchers are also developing pedagogical approaches or theories for mobile learning
(Dennen & Hao, 2014; Park, 2011). Other researchers have provided strategies for integrating mobile
learning and overcoming challenges to mobile learning implementation (Brown & Mbati, 2015;
Cochrane, 2014). A few studies have also examined differences in learners and faculty by studying users
within mobile learning (Mac Callum, Jeffrey, & Kinshuk, 2013; Lin, Zimmer, & Lee, 2013). Educators
are also interested in learning within classes and out of classes. In-class systems may include student
response systems (Calma, Webster, Petry, & Pesina, 2014), while researchers are also interested in
informal learning outside of classrooms (Reychav, Kobayashi, & Dunaway, 2015). Innovative learning
approaches include a variety of different approaches. Studies have used context-aware mobile learning
services to personalise learning (Lu, Chang, Kinshuk, Huang, & Ching-Wen, 2014; Wu, Hwang, Su, &
Huang, 2012) or made use of mobile augmented reality (Fonseca, Martí, Redondo, Navarro, & Sánchez,
2014). The use of gamification has been used to promote motivation (Bartel & Hagel, 2014). Learner
mobility studies have focused on learners using devices for collaborative learning in the field (Redondo,
Fonseca, Sánchez, & Navarro, 2014). Another area of interest for educators and researchers is the use of
assessment and evaluation. For example, integrating the use of mobile quizzes into learning processes
(Bogdanović, Barać, Jovanić, Popović, & Radenković, 2014). Researchers have focused on integrating
cloud computing into mobile learning (Wang, Chen, & Khan, 2014). Researchers have also investigated
how mobile learning is researched and implemented, through review studies (Baran, 2014). With

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increasing amounts of data available, educators are interested in using learning analytics to understand
and optimise learning processes and environments (Tabuenca, Kalz, Drachsler, & Specht, 2015).
Theoretical and Conceptual Backgrounds
Every research study should have clear theoretical or conceptual backgrounds (Bozkurt et al., 2015). In
classifying the theoretical and conceptual backgrounds specified in research articles, insights may be
provided regarding the kinds of topics and how researchers are approaching them in mobile learning in
higher education. Where stated, the theories and or concepts stated in the articles were included in the
categorisation, adapted from the classification by Bozkurt et al. (2015). Table 2 lists the most frequently
stated theories or underlying concepts. In several articles, multiple theoretical or conceptual
backgrounds were used together; however, Table 2 only highlights the frequency of the theoretical or
conceptual backgrounds in the population.
The most frequently stated theoretical or conceptual backgrounds model how users come to accept and
use a new technology (Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of
Technology (UTAUT), and Diffusion of Innovation). A strong emphasis can be seen on collaboration
within a community (Collaborative Learning, Communities of Practice). Another trend can be seen to
be moving to a learner-oriented paradigm focussing on student experiences in a social world (Activity
Theory, Social Constructivism, Constructivism) and authentic learning experiences (Authentic
Learning). With the affordances of mobile technologies leading to educators redesigning their curricula
or modes of provision, instructional design theories are also important (Cognitive Load Theory,
Instructional Design).
Table 2
Distribution of Most Common Theoretical or Conceptual Backgrounds
Rank
Theoretical / Conceptual background
Frequency
1
Technology Acceptance Model (TAM)
23
2
Unified Theory of Acceptance and Use of Technology
(UTAUT)
9
3
Collaborative Learning
6
4
Activity Theory / Systems
5
4
Cognitive Load Theory
5
4
Diffusion of Innovation
5
4
Self-regulated / Self-managed Learning
5
8
Authentic Learning
4
8
Communities of Practice
4
8
Learning Styles
4
8
Scaffolded Learning
4
8
Social Constructivism
4
8
Socio-cultural Theory
4
8
Technological Pedagogical And Content Knowledge
(TPACK)
4
15
Adaptive Learning
3
15
Constructivism
3
15
Cultural Dimensions
3
15
Instructional Design
3
15
Substitution Augmentation Modification Redefinition
(SAMR)
3

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No comparison can be done with the research studies from 2001-2010 as the theoretical and conceptual
background was not a specific focus of the studies of Hwang and Tsai (2011), Hung and Zhang (2012),
and Wu et al. (2012).
Research Designs
A mobile learning study generally employs a quantitative, qualitative, or mixed research method like
other educational fields (Bozkurt et al., 2015). In this review, researchers in mobile learning in higher
education mostly conducted qualitative research (46%) or quantitative research (43%), with fewer
studies employing mixed methods (11%). No comparison can be done with the research studies from
2001-2010 as the research method was not a specific focus of the studies of Hwang and Tsai (2011),
Hung and Zhang (2012), and Wu et al. (2012).
In addition to the research method, the research design can also be explored within each of the methods.
The methods used to categorise the research were adapted from Bozkurt et al. (2015) and Creswell
(2009). Table 3 indicates that the most commonly used research designs for quantitative studies were
descriptive surveys (17%), followed by correlational studies (13%), and experiments (12%). Table 3 also
indicates that the most commonly used research design for qualitative studies was design-based
research (18%), followed by case studies (17%), and action research (3%). For mixed methods, the most
common research designs used were sequential explanatory (7%), concurrent triangulation (3%), and
sequential exploratory (1%).
Table 3
Distribution of Studies by Research Design
Quantitative (43%)
Qualitative (47%)
Mixed (11%)
Case Study
1%
Action Research
3%
Concurrent
Triangulation
3%
Correlational
13%
Case Study
17%
Sequential
Explanatory
7%
Experiment
12%
Content Analysis
3%
Sequential
Exploratory
1%
Survey
17%
Design-based
18%
Grounded Theory
1%
Meta-synthesis
2%
In terms of comparison with 2001-2010, the quantitative method findings closely align to the findings
of Wu et al. (2012). They found the most common methods for quantitative studies to be experiments
and descriptive research. However, the qualitative methods are different in that Wu et al. (2012) did not
find case studies, action research, nor other qualitative methods to be widely used. A caution must be
noted though that Wu et al. (2012) presented their results with a different classification and integrated
the presentation of results for both research methods and data collection methods.
Data Collection
Data collection methods were also investigated in this study. Methods were coded into seven categories,
adapted from Song (2014) and Cheung and Hew (2009). Table 4 shows that the most common method
used was a survey (47%) followed by interviews/focus groups (18%) and assessments (13%). Studies
utilised between one and five data collection methods, with 57% of studies utilising one method and 28%

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of studies utilising two methods. Twelve percent of studies utilised three methods, while 3% utilised four
methods.
Table 4
Distribution of Studies by Data Collection Method
Method
Instruments or techniques
Frequency
Assessment
Tests or quizzes
13%
Document Review
Examination of documents
5%
Interviews/Focus
Groups
Discussions between researchers, staff, or students
18%
Observation
Visual examination and documenting actions and utterances of
participants, either directly or via recording
3%
Process Data
Estimates of time, frequency and sequence as well as tracing
data and learning analytics obtained from systems and devices
6%
Product Data
All outputs produced by participant activities such as course
assignments
7%
Survey
Questionnaires, surveys, and scales
47%
In comparison with studies from 2001-2010, the collection method findings do align somewhat to the
findings of Wu et al. (2012) in that surveys continue to be the most common format of collecting data.
However, the current study results seem to indicate that a wider range of data collection methods were
used (2011-2015) than previously.
Population Groups
It was found that the vast majority of studies were aimed at students (78%). A few studies focused on
faculty (10%) or a combination of both faculty and students (12%). Of the studies that focused on
students, 75 studies distinguished between undergraduate and postgraduate levels of students. Of these
studies, 81% studies focussed on undergraduates and 19% focused on postgraduate students. As both
faculty and student adoption play a part in the success of mobile learning initiatives, it is recommended
that more studies in the future look to investigate the implications for both faculty and students. A major
difference between this study and previous studies by Hwang and Tsai (2011) and Wu et al. (2012) is
that this study only focused on the higher education sector. However, both Hwang and Tsai (2011) and
Wu et al. (2012) similarly found that the majority of mobile learning studies across all sectors focused
on higher education students.
Academic Disciplines
Wu et al. (2012) define an academic discipline as a branch of knowledge that is taught or researched at
the higher education level. This study follows the discipline taxonomy used by Wu et al. (2012) who
adopted it from the taxonomy developed by Becher (1994), Wanner, Lewis, and Gregorio (1981), and
others. This taxonomy identifies five major categories of academic discipline: humanities, social
sciences, natural sciences, formal sciences, and professions and applied sciences. Academic subjects
listed in the Classification of Instructional Programs (CIP) (Institute of Education Sciences, 2010) can
be classified within these disciplines. These disciplines and subjects are listed in Table 5. A third (33%)
of mobile learning studies in higher education are across disciplines (generic) or not discipline-specific.
If the remaining studies are classified according the above taxonomy, the most frequent are professions
and applied sciences (34%), followed by humanities (16%), formal sciences (11%), social sciences (3%),

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and natural sciences (3%). In terms of individual sub-disciplines, languages and linguistics was the most
common focus (35 studies), followed by education (28 studies), computer science (26 studies), and
health sciences (26 studies).
Table 5
Distribution of Disciplines and Sub-disciplines
Discipline
Subject
Number of
studies
1. Humanities (16%)
1.1 History
0
1.2 Languages and Linguistics
35
1.3 Literature
0
1.4 Performing Arts
0
1.5 Philosophy
0
1.6 Religion
1
1.7 Visual Arts
3
2. Social Sciences
(3%)
2.1 Anthropology
0
2.2 Archaeology
0
2.3 Area Studies
0
2.4 Cultural & Ethnic Studies
1
2.5 Economics
0
2.6 Gender & Sexuality Studies
0
2.7 Geography
3
2.8 Political Science
0
2.9 Psychology
2
2.10 Sociology
2
3. Natural Sciences
(3%)
3.1 Space Sciences
1
3.2 Earth Sciences
2
3.3 Life Sciences
1
3.4 Chemistry
2
3.5 Physics
0
4. Formal Sciences
(11%)
4.1 Computer Science
26
4.2 Logic
0
4.3 Mathematics
2
4.4 Statistics
0
4.5 Systems Science
0
5. Professions /
Applied Sciences
(34%)
5.1 Agriculture
0
5.2 Architecture & Design
5
5.3 Business
12
5.4 Divinity
0
5.5 Education
28
5.6 Engineering
8
5.7 Environmental Studies and Forestry
1
5.8 Family and Consumer Science
0
5.9 Health Sciences
26
5.10 Human Physical Performance and Recreation
0
5.11Journalism, Media Studies and Communication
1
5.12 Law
0
5.13 Library and Museum Studies
2
5.14 Military Science
0
5.15 Public Administration
0
5.16 Social Work
0
5.17 Transportation
0
Generic (Across
Disciplines) (30%)
Generic (Across Disciplines)
81

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In terms of comparison with studies from 2001-2010, these findings closely align to the studies by
Hwang and Tsai (2011) and Wu et al. (2012). Wu et al. (2012) found that the most common disciplines
to be professions and applied sciences (29%), humanities (20%), and formal sciences (16%). Similar to
findings by Hwang and Tsai (2011), a significant proportion of studies do not focus on a specific
discipline, but are generic or across disciplines. Thus, it can be seen that mobile learning continues to
be applied across most disciplines and that researchers from different disciplines can collaborate. In
terms of sub-disciplines or subjects, the present study has similar findings that languages and
linguistics, computer science, and health sciences are well represented. Language and health science
educators seem to be more eager to adopt the affordances of mobile learning, where practical benefits
can be seen for students. Mobile-assisted language learning (MALL) is a particularly growing area
(Viberg & Gronlund, 2013; Wu, 2015). The present study shows that the education discipline has become
more of a focus for researchers. It is theorised that educators in computer science and education may be
more prone to take advantage of technological innovations in learning. Nonetheless, more studies are
required that show how mobile learning is adopted in other academic subjects. For future research at a
category level, it is recommended that more research studies be conducted in the natural and social
sciences.
Research Settings
Figure 3 shows the distribution of research settings. The categories of research settings were adapted
from Song (2014) and Zheng, Huang, & Yu (2014). Most often, research was carried out in both in class
and out of class settings (33%), followed by research carried out in class settings (16%) and research
conducted across settings (15%). Research also took place in field settings, out of class settings, and in
distance settings. More studies are needed in the future that focus on learner mobility and transitions
across different settings.
Figure 3. Distribution of studies by research setting.
No comparison can be done with the research studies from 2001-2010 as research settings were not a
specific focus of the studies of Hwang and Tsai (2011), Hung and Zhang (2012), and Wu et al. (2012).

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Devices
Figure 4 shows the distribution of mobile devices used in the studies from 2011 to 2015. As indicated,
the majority of studies (107) studied non-specific / generic mobile devices or learning across mobile
devices. This may indicate that as technology changes so quickly, it may be best not to invest in a specific
device as mobile learning can take place across a multitude of devices. This result may also be indicative
of the growing realisation of Bring-Your-Own-Device (BYOD) (Cochrane, Antonczak, Keegan, &
Narayan, 2014; Traxler, 2016). If one looks at the specific device trends, it is clear that mobile phones
(including smartphones) are the most frequently used devices in studies (73). It must be noted that 38
of the 73 studies using mobile phones specified the use of smartphones in particular. Tablets are also
very frequently used in studies (33). For those studies that reported the specific brand of tablet, the
Apple iPad was the overwhelmingly most used tablet brand.
Figure 4. Distribution of devices by year.
In terms of comparison with studies from 2001-2010, the results demonstrate the changes in available
technologies since the study conducted by Wu et al. (2012). However, mobile phones are still the most
common devices used in studies. An increasing number of studies have focused on the use and
affordances of smartphones (for example, the use of specific apps) rather than basic phones and features
(for example, text messaging). Changes in available devices and emerging technologies influence the
studies that are conducted. For example, previous studies made significant mention of PDA devices,
whereas in the more recent studies from 2011-2013, these are seldom mentioned, and not mentioned at
all in 2014-2015 studies. Tablet devices, particularly the Apple iPad, launched in 2010, have become
much more prevalent.

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Discussion
The results of this study reveal research trends and issues in mobile learning in higher education. Mobile
learning continues to be a growing area of research in higher education as evidenced by the number of
academic articles published between 2011 and 2015 and the number of countries where this research
was conducted. Forty-five countries were represented in this study. The results of this study have several
implications for future research in mobile learning in higher education.
Need for Expansion of Focus of Research Themes
The most common research purpose was found to be evaluating the effectiveness of mobile learning
(24%), followed by the design of a mobile system for learning (23%). This study found that the three
most common research themes together (mobile applications and systems; socio-cultural contexts; and
tools and technologies) account for almost half of the mobile learning studies in higher education (48%).
Figure 5 shows the research themes according to research purpose. This figure shows that there are
several themes that are underrepresented in current studies. Consideration of those themes that have
fewer studies should lead to researcher reflection and more studies in those areas to lead to a more
complete understanding of the field. As a growing research field, the themes within mobile learning in
higher education will change over time. However, several themes merit specific attention. More research
and practice is required in themes related to innovative approaches (such as context-awareness services,
augmented reality, and gamification). Additionally, studies that focus on learner mobility and
transitions across different settings are areas where more research is needed. Finally, the use of newer
technologies such as cloud computing and learning analytics may become greater themes of focus for
researchers.

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Figure 5. Research themes and purposes radar chart.
Promotion of Variety in Research Design
In terms of research methodology, both qualitative (46%) and quantitative (43%) approaches were used
most often, with the remainder of studies utilizing a mixed methods approach. A variety of research
designs were employed by researchers; the most common data collection methods were surveys (47%),
interviews/focus groups (18%), and assessments (13%). These findings align closely with studies from
2001-2010, but it appears that a wider variety of methods are increasingly being utilised. For future
studies, it is recommended that authors are clear in describing the methodology used in their studies
and include the theoretical/conceptual background, research design, data collection methods, data
analysis approach, population groups, academic discipline, and research setting. Due to the various
research topics and approaches in this expanding research field, there is a need for a wide range of
research designs. However, the authors would like to point out that more studies in the future should
look to make use of mixed methods research approaches. These approaches can combine the strengths
of quantitative and qualitative methodologies. It is further recommended that more longitudinal studies
are required, as well as studies across more than one individual course in order to understand the long-
term effects and impact of mobile learning initiatives. This will also assist with understanding issues
around sustainability and scale. Fewer studies are required that compare the mode of teaching and
learning (mobile learning or e-learning). This is because of the many variable conditions within a mode
of teaching and learning. Researcher attempts to keep all other conditions the same, can lead to a
suppression of the conditions that may flourish in a particular mode (Bates & Sangra, 2011).
Growth of Bring-Your-Own-Device (BYOD) and Multiple Devices

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A key finding from the study was that a significant proportion of studies did not focus on a specific device
for learning, and instead focused on a generic device or on multiple devices. For studies where a device
was specified, mobile phones (including smartphones) were the devices most commonly used in studies,
followed by tablets. Increasingly, educators and researchers cannot rely on funding for studies where
students or staff are provided with specific devices for learning. Further studies are required that look
at the personal devices that students have access to and how they access content and university services
from these devices. However, BYOD goes beyond access to devices as students are no longer limited to
institutional systems, but increasingly have their own internet access and make use of their own services.
Devices are important, but the associated systems and networks are equally significant (Traxler, 2016).
Access and use of these devices by a majority of students presents challenges and opportunities for the
support and provision of learning (Traxler, 2010). Further research is required in how BYOD strategies
are incorporated into university teaching and learning and the provision of associated academic and
technological support. For the successful integration of mobile learning, faculty need to critically assess
the use of mobile devices for learning and design specific learning experiences that take advantage of
the affordances of mobile devices. Otherwise, mobile learning may continue to be restricted to viewing
a mobile version of an institutional learning management system. Very often, students have access to
more than one personal device. Students may use of multiple devices and these devices can change over
time. New technologies arrive all the time, enabling faculty and students to explore new ways to learn
with these tools (Parsons, 2014). For example, future studies may focus on the impact of wearable
technologies in learning.
Focus on Sustainability and Mainstreaming of Mobile Learning
Increasingly, advanced mobile technologies have become integrated into society, but despite the
potential, have not yet been “fully and formally integrated into higher education” (Traxler, 2016,
“Looking backward”, para. 3). Many innovative research projects in mobile learning in the last 15 years
did not extend beyond pilot projects to become embedded or mainstreamed in education, in part
because of financial and cultural barriers (Traxler, 2016). Further research into how mobile learning
studies can be scaled up or embedded into higher education institutions would be useful. It is expected
that in the next 10 years, mobile technologies will continue to become more popular, personal, and
social. This means that mobile and connected learners can potentially change the nature of teaching and
learning. With the aid of mobile technologies, students can easily “generate, store, share, discuss and
consume images, ideas, information and opinions, can access the cloud, and the services it provides, and
can access each other” (Traxler, 2016, “Looking forward,” para. 8). Often this takes place outside of
institutional systems and applications. This has profound implications for how faculty design courses
and facilitate learning.
Conclusion
Similar to previous review studies, this research aims to provide analysis and guidance for the selection
of research topics and methods within mobile learning (Hung & Zhang, 2012). Systematic reviews can
generate suggestions and insightful implications for researchers and educators aiming to provide
meaningful mobile learning experiences and environments (Hsu & Ching, 2015). The reviews of Hwang
and Tsai (2011), Hung and Zhang (2012), and Wu et al. (2012) applied to research studies from 2001
until 2010. This study examined articles from 2011 to 2015 as follow up research to consider the

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similarities and differences in an expanding field. This research focused solely on the higher education
context. Following a search of three academic databases, 233 peer-reviewed articles were selected and
organised for review. The researchers used content analysis to analyse the data around categories related
to research purpose, theme, method, target population, setting, device, and others. In comparison with
previous reviews, similarities were found with regard to research purposes and research methods used.
Key findings indicate that researchers conduct studies in mobile learning in higher education for a
variety of reasons, but that evaluating the effectiveness is the most common purpose. Similarly, a variety
of themes within mobile learning are explored, but the most common topic focuses on enabling
applications and systems. An increasing number of studies have focused on the use and affordances of
smartphones (for example, the use of specific apps) rather than basic phones and features (for example,
text messaging). Newer research topics relate to mobile learning and social networking, games and
augmented reality. Research methods are split between quantitative and qualitative methods. Data
collection continues to focus primarily on surveys, but a wider variety of methods is being utilised. A
significant proportion of studies do not focus on a specific mobile device, but across devices in mobile
learning. The research shows the increasing trend of BYOD. Mobile phones are still the most common
devices used in mobile learning studies (including smartphones), but tablets are increasingly popular. A
significant change is occurring through BYOD, where learning with multiple personal devices is possible.
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